Characterizing the Geometry of Internal Representations using Massive Online Experiments in Audition and Vision

Our auditory and visual memory systems encode information selectively due to limited resources, resulting in systematic distortions and biases. Understanding these biases allows us to characterize the latent geometry of our mind, namely, to better understand how the external world is mapped onto internal representations. Massive online experiments, which provide access to huge and diverse participant pools, afford new opportunities for studying the rich and complex perceptual space of internal representations with unprecedented resolution. This project uses novel interactive paradigms based on Markov Chain Monte Carlo with People (Sanborn & Griffiths 2008), together with classical psychophysics adapted to online environments. It includes the development of experiments implementing both frontend (js) and backend (python) using the Dallinger platform for laboratory automation for the behavioral and social sciences (http://dallinger.readthedocs.io/).